Hari Prasanna Das
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PhD Candidate Department of Electrical Engineering and Computer Sciences University of California, Berkeley Email: hpdas (at) berkeley.edu Office: CREST Lab, 406 Cory Hall Google Scholar |
Brief Biography
I am a PhD candidate in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, advised by Prof. Costas J. Spanos. I am also a Graduate Student Researcher for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) program. My research lies at the intersection of Deep Semi-supervised Learning, Smart and Energy-efficient Buildings, and Climate Change Mitigation.
I graduated from Indian Institute of Technology (IIT) Kharagpur with a B.Tech.(Honors) in Electrical Engineering in 2016. During my undergraduate studies, I was fortunate to work with Prof. Ashok Pradhan on several projects. Prior to joining UC Berkeley, I worked for a year at Mentor Graphics, India as a R&D Engineer.
News
April 2021: I am co-organizing the "Tackling Climate Change with Machine Learning" workshop at International Conference on Machine Learning (ICML) 2021, a leading conference on ML. Please consider submitting your relevant research works.
April 2021: I moderated a panel discussion on "The Smart Grid: Harnessing the Power of AI" at the AI LA Earth Summit. [Recording here]
April 2021: Our paper "Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data" has been accepted at ICLR 2021 Workshop on Machine Learning for Preventing and Combating Pandemics.
March 2021: We are organizing "3rd International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES)", co-located with ACM e-Energy Conference 2021 (Virtual). Please consider submitting your work. [Call for Papers]
- January 2021: I gave a talk on "Tackling Climate Change with Machine Learning" at the Terra.do climate school. [Recording here]
November 2020: I joined Climate Change AI, an organization catalyzing impactful work at the intersection of climate change and machine learning.
October 2020: Our paper "Do Occupants in a Building exhibit patterns in Energy Consumption? Analyzing Clusters in Energy Social Games" has been accepted at NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning.
August 2020: I am serving as the Head Content GSI (Graduate Student Instructor) for the upper division class "EECS 127/227AT: Optimization Models in Engineering" in its Fall 2020 offering with Prof. Venkat Anantharam.
August 2020: I am co-organizing the Doctoral Consortium on Computational Sustainability 2020, to be held virtually from Oct 17-18, 2020. [Apply here by Sept 11th to participate]
May 2020: Our short paper on "Understanding Distributions of Environmental Parameters for Thermal Comfort Study in Singapore" has been accepted at AMLIES workshop, ACM e-Energy Conference 2020.
April 2020: Research happening in our group has been covered by Wired Magazine. Read it here!
February 2020: Our paper "BISCUIT: Building Intelligent System CUstomer Investment Tools" has been accepted at ICLR 2020 Workshop on Tackling Climate Change with Machine Learning.
February 2020: I am co-organizing the Computational Sustainability Open Graduate Seminar (COGS) series for Spring 2020. The seminars will be every alternate Friday 1.30PM-2.30PM Eastern Time, starting 07th Feb 2020. Please join the meeting if you are interested.
January 2020: We are organizing "2nd International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES)", co-located with ACM e-Energy Conference 2020 at Melbourne, Australia. Please consider submitting your work. [Call for Papers]
October 2019: Our paper "Design, Benchmarking and Graphical Lasso based Explainability Analysis of an Energy Game-Theoretic Framework" has been accepted at NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning.
October 2019: Our paper "A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks" has been accepted at ICMLA 2019.
September 2019: Selected to attend Doctoral Consortium on Computational Sustainability at CMU, with a travel funding from NSF.
August 2019: I am in the Technical Program Committee (TPC) of NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning.
August 2019: I am a Graduate Student Instructor (GSI) for the upper division class "EECS 127/227AT: Optimization Models in Engineering" in its Fall 2019 offering with Prof. Alexandre M. Bayen.
July 2019: Our paper "Personal thermal comfort models with wearable sensors" has been accepted for publication in the Building and Environment Journal.
May 2019: Our paper "Machine Learning empowered Occupancy Sensing for Smart Buildings" has been accepted at Climate Change and AI Workshop, International Conference on Machine Learning (ICML) 2019
April 2019: Our paper "WiFi and Vision Multimodal Learning for Accurate and Robust Device-Free Human Activity Recognition" has been accepted at MULA Workshop, Conference on Computer Vision and Pattern Recognition (CVPR) 2019
January 2019: We are organizing "International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES)", co-located with ACM e-Energy Conference 2019 at Phoenix, Arizona, USA. Please consider submitting your work. [Call for Papers]
December 2018: I am invited to attend the Global Young Scientists Summit (GYSS), 2019 to be held at Singapore in January 2019
November 2018: Our paper "Consensus Adversarial Domain Adaptation" has been accepted at AAAI Conference on Artificial Intelligence 2019
June 2018: Our paper "BISCUIT: Building Intelligent System CUstomer Investment Tool" has been accepted at International Conference on Applied Energy 2018
January 2018: Our paper "Personal thermal comfort models based on physiological parameters measured by wearable sensors" has been accepted at Windsor Conference 2018
